Meta-analysis on gene regulatory networks discovered by pairwise Granger causality

Identifying regulatory genes partaking in disease development is important to medical advances. Since gene expression data of multiple experiments exist, combining results from multiple gene regulatory network discoveries offers higher sensitivity and specificity. However, data for multiple experiments on the same problem may not possess the same set of genes, and hence many existing combining methods are not applicable. In this paper, we approach this problem using a number of meta-analysis methods and compare their performances. Simulation results show that vote counting is outperformed by methods belonging to the Fisher's chi-square (FCS) family, of which FCS test is the best. Applying FCS test to the real human HeLa cell-cycle dataset, degree distributions of the combined network is obtained and compared with previous works. Consulting the BioGRID database reveals the biological relevance of gene regulatory networks discovered using the proposed method.published_or_final_versio

Application of Granger causality to gene regulatory network discovery

Article no. 6314142Granger causality (GC) has been applied to gene regulatory network discovery using DNA microarray time-series data. Since the number of genes is much larger than the data length, a full model cannot be applied in a straightforward manner, hence GC is often applied to genes pairwisely. In this paper, firstly we investigate with synthetic data and point out how spurious causalities (false discoveries) may emerge in pairwise GC detection. In addition, spurious causalities may also arise if the order of the vector autoregressive model is not high enough. Therefore, besides using a suitable model order, we recommend a full model over pairwise GC. This is possible if pairwise GC is first used to identify a network of interactions among only a few genes, and then all these interactions are validated with a full model again. If a full model is not possible, we recommend using model validation techniques to remove spurious discoveries. Secondly, we apply pairwise GC with model validation to a real dataset (HeLa). To estimate the model order, the Akaike information criterion is found to be more suitable than the Bayesian information criterion. Degree distribution and network hubs are obtained and compared with previous publications. The hubs tend to act as sources of interactions rather than receivers of interactions. © 2012 IEEE.published_or_final_versio

PillTank

Imagine an elderly family member, going through their daily routine of taking their pills. They find their pill box; however, they are having trouble identifying all the pills in there. Is there a name on the tablet? Can they read what it says? Do they just trust that the medication in their box is correct? How can they properly take care of themselves if they can not even confirm that what they are taking is the right medication? To combat this issue that many face, we present PillTank. To decrease the risk of consuming the wrong medication, PillTank identifies the pills being taken and ensures people, especially senior citizens, are correctly adhering to their treatment. To make PillTank simple, it is stripped down to its bare bones - a platform, camera, and an electronic screen. Emphasizing accessibility, accuracy, and speed, the user can simply place their pills on PillTank’s platform and a recognition algorithm will run. A detailed description of the pills will then be displayed on a large electronic screen. The history of scanned pills is then saved, which can provide family members or healthcare professionals insight as to what medication someone may be taking. While the initial form is a stand-alone device, PillTank can be expanded on to include voice recognition and an audio feedback system. Depending on the use case, this flexibility allows us to broaden our audience, all while ensuring that people stay safe and healthy by knowing what pills they are taking. Overall, PillTank removes the challenge of reading the fine print on pills by identifying them for the user, and allows users to consume their medication responsibly and safely

The information role of advisors in mergers and acquisitions: evidence from acquirers hiring targets’ ex-advisors

We examine the information role of financial advisors by focusing on mergers and acquisitions in which acquiring firms hire target firms’ ex-advisors. We document that by employing targets’ ex-advisors, acquirers pay lower takeover premiums and secure a larger proportion of merger synergies. The corresponding targets exhibit lower announcement returns and are less likely to be propositioned by competing bidders. These results indicate that acquirers take advantage of value-relevant information about targets through targets’ ex-advisors, and achieve bargaining advantages in deal negotiations. In contrast, we document no discernible value effects when targets hire acquirers’ ex-advisors, suggesting that the information role of acquirers’ ex-advisors hired by targets is weaker than that of targets’ ex-advisors hired by acquirers

Sex Differences in Health Related Quality of Life in Head & Neck Cancer One Year After Treatment

Background: Head and neck cancer (HNC) makes up about 3% of all cancers and is treated with systemic therapy, radiation, surgery, or a combination of these. HNC treatment can be associated with decreased patient reported health related quality of life (HR-QoL), which can lead to depression. The majority of studies found that females reported worse patient reported HR-QoL than males, however, there were a few that did not have a significant difference in overall patient reported QoL. With the discovery of patient oriented outcomes (PROs) in clinical practice affecting patient satisfaction, provider-patient relationship, and overall patient mortality, it is vital to include PROs in the creation of treatment plans. Objectives: The objectives of this project are to highlight the differences in HR-QoL between men and women. Ultimately, using these PROs clinically will help to improve patient care, augment patient-provider trust, and optimize treatment plans. Using PROs and recognizing where unconscious biases of providers come into play is pinnacle, and this project aims to highlight how men and women\u27s experiences are different in the treatment of HNC. Methods: Participants were given the FACT-H&N instrument one year after treatment for head and neck cancer at a single tertiary academic center to assess different aspects of Hr-QoL. Sex differences were analyzed between the groups. A Wilcoxon Rank Sum test was performed to assess associations with sex and survey responses, as well as to assess associations with total laryngectomy and survey responses. Results: There were 100 participants from a single academic center of which 73% were men and 27% women. Several of the questions had significant differences between men and women: I feel ill (P=0.0299), I am satisfied with my family communication about my illness P=0.0075), I am satisfied with my sex life (P=0.0496), My voice has its usual quality and strength (P=0.0057), I can swallow naturally and easily (P=0.0437), and I can eat solid foods (P=0.0248). There were no significant differences between men and women with laryngectomies. Conclusions: Overall, men felt more ill, were less satisfied with their sex lives, were less likely to feel a normal strength and quality of voice, felt decreased ability to swallow normally, and felt they could not eat solid foods; women were less satisfied with communication about their disease to their families. For those who had undergone laryngectomy, there were no significant differences between men and women. Different aspects of quality of life for men and women are affected by head and neck cancer. Monitoring PROs are becoming increasingly standard of care for patients, and providers need to be equipped understand how to interpret data accordingly and understand the inherent biases

Cognitive and affective perspectives on formation and maintenance of grandiose delusions of a patient with schizophrenia

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